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Dermatology Practical & Conceptual
By Eren Çamur, İstem Şanal Çamur, Selçuk Parlak and 1 others.
Dermatologic ultrasonography is an commonly utilized, operator-dependent imaging modality in clinical practice, yet the role of multimodal artificial intelligence in this domain remains insufficiently defined, particularly in the absence of structured clinical input.
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To evaluate whether retrieval-based knowledge integration and structured radiologist reporting improve the diagnostic performance of a vision-capable large language model in dermatologic ultrasound for increasing their utility in clinical practice.
Dermatologic ultrasound cases with histopathological reference standards were assessed under four clinically relevant conditions: image-only without retrieval, image-only with retrieval, image plus radiologic report without retrieval, and image plus radiologic report with retrieval. A vision-capable large language model was used without fine-tuning. Outputs were evaluated for most likely diagnosis accuracy and differential diagnosis performance.
Image-only performance was limited and did not significantly improve with retrieval augmentation. In contrast, the incorporation of structured radiologic reports resulted in a marked improvement in diagnostic accuracy and differential diagnosis generation. The addition of retrieval further enhanced performance, with the highest accuracy observed in the image-plus-report with retrieval condition, indicating a synergistic effect between structured input and knowledge augmentation.
The effectiveness of artificial intelligence in dermatologic imaging, is primarily determined by input structure rather than retrieval alone. Retrieval-based methods provide meaningful benefit only when coupled with expert-derived semantic representations, supporting a clinically integrated, collaborative model of artificial intelligence use rather than standalone image interpretation.
Published in Dermatology Practical & Conceptual. The abstract is shown as published and remains the work of its authors; read the full article at the source.
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